# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import OrderedDict import paddle.fluid as fluid from ppdet.experimental import mixed_precision_global_state from ppdet.core.workspace import register __all__ = ['CascadeRCNN'] @register class CascadeRCNN(object): """ Cascade R-CNN architecture, see https://arxiv.org/abs/1712.00726 Args: backbone (object): backbone instance rpn_head (object): `RPNhead` instance bbox_assigner (object): `BBoxAssigner` instance roi_extractor (object): ROI extractor instance bbox_head (object): `BBoxHead` instance fpn (object): feature pyramid network instance """ __category__ = 'architecture' __inject__ = [ 'backbone', 'fpn', 'rpn_head', 'bbox_assigner', 'roi_extractor', 'bbox_head' ] def __init__(self, backbone, rpn_head, roi_extractor='FPNRoIAlign', bbox_head='CascadeBBoxHead', bbox_assigner='CascadeBBoxAssigner', rpn_only=False, fpn='FPN'): super(CascadeRCNN, self).__init__() assert fpn is not None, "cascade RCNN requires FPN" self.backbone = backbone self.fpn = fpn self.rpn_head = rpn_head self.bbox_assigner = bbox_assigner self.roi_extractor = roi_extractor self.bbox_head = bbox_head self.rpn_only = rpn_only # Cascade local cfg self.cls_agnostic_bbox_reg = 2 (brw0, brw1, brw2) = self.bbox_assigner.bbox_reg_weights self.cascade_bbox_reg_weights = [ [1. / brw0, 1. / brw0, 2. / brw0, 2. / brw0], [1. / brw1, 1. / brw1, 2. / brw1, 2. / brw1], [1. / brw2, 1. / brw2, 2. / brw2, 2. / brw2] ] self.cascade_rcnn_loss_weight = [1.0, 0.5, 0.25] def build(self, feed_vars, mode='train'): im = feed_vars['image'] assert mode in ['train', 'test'], \ "only 'train' and 'test' mode is supported" if mode == 'train': required_fields = ['gt_label', 'gt_box', 'is_crowd', 'im_info'] else: required_fields = ['im_shape', 'im_info'] for var in required_fields: assert var in feed_vars, \ "{} has no {} field".format(feed_vars, var) im_info = feed_vars['im_info'] if mode == 'train': gt_box = feed_vars['gt_box'] is_crowd = feed_vars['is_crowd'] mixed_precision_enabled = mixed_precision_global_state() is not None # cast inputs to FP16 if mixed_precision_enabled: im = fluid.layers.cast(im, 'float16') # backbone body_feats = self.backbone(im) # cast features back to FP32 if mixed_precision_enabled: body_feats = OrderedDict((k, fluid.layers.cast(v, 'float32')) for k, v in body_feats.items()) # FPN if self.fpn is not None: body_feats, spatial_scale = self.fpn.get_output(body_feats) # rpn proposals rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) if mode == 'train': rpn_loss = self.rpn_head.get_loss(im_info, gt_box, is_crowd) else: if self.rpn_only: im_scale = fluid.layers.slice( im_info, [1], starts=[2], ends=[3]) im_scale = fluid.layers.sequence_expand(im_scale, rpn_rois) rois = rpn_rois / im_scale return {'proposal': rois} proposal_list = [] roi_feat_list = [] rcnn_pred_list = [] rcnn_target_list = [] proposals = None bbox_pred = None for i in range(3): if i > 0: refined_bbox = self._decode_box( proposals, bbox_pred, curr_stage=i - 1, ) else: refined_bbox = rpn_rois if mode == 'train': outs = self.bbox_assigner( input_rois=refined_bbox, feed_vars=feed_vars, curr_stage=i) proposals = outs[0] rcnn_target_list.append(outs) else: proposals = refined_bbox proposal_list.append(proposals) # extract roi features roi_feat = self.roi_extractor(body_feats, proposals, spatial_scale) roi_feat_list.append(roi_feat) # bbox head cls_score, bbox_pred = self.bbox_head.get_output( roi_feat, wb_scalar=1.0 / self.cascade_rcnn_loss_weight[i], name='_' + str(i + 1) if i > 0 else '') rcnn_pred_list.append((cls_score, bbox_pred)) if mode == 'train': loss = self.bbox_head.get_loss(rcnn_pred_list, rcnn_target_list, self.cascade_rcnn_loss_weight) loss.update(rpn_loss) total_loss = fluid.layers.sum(list(loss.values())) loss.update({'loss': total_loss}) return loss else: pred = self.bbox_head.get_prediction( im_info, feed_vars['im_shape'], roi_feat_list, rcnn_pred_list, proposal_list, self.cascade_bbox_reg_weights, self.cls_agnostic_bbox_reg) return pred def _decode_box(self, proposals, bbox_pred, curr_stage): rcnn_loc_delta_r = fluid.layers.reshape( bbox_pred, (-1, self.cls_agnostic_bbox_reg, 4)) # only use fg box delta to decode box rcnn_loc_delta_s = fluid.layers.slice( rcnn_loc_delta_r, axes=[1], starts=[1], ends=[2]) refined_bbox = fluid.layers.box_coder( prior_box=proposals, prior_box_var=self.cascade_bbox_reg_weights[curr_stage], target_box=rcnn_loc_delta_s, code_type='decode_center_size', box_normalized=False, axis=1, ) refined_bbox = fluid.layers.reshape(refined_bbox, shape=[-1, 4]) return refined_bbox def train(self, feed_vars): return self.build(feed_vars, 'train') def eval(self, feed_vars): return self.build(feed_vars, 'test') def test(self, feed_vars): return self.build(feed_vars, 'test')